Is the growing cost of Nvidia-based compute a business moat or a liability? And in either case, which contestants benefit or suffer?
A new Morgan Stanley analysis suggests the cost of Nvidia's GB200 systems now cost about $35 billion per gigawatt (GW) of computing capacity, up 16 percent from prior estimates.
GB300 clusters rise to $39 billion per GW, while Vera Rubin-based systems jump nearly 20% to $49 billion per GW.
Those estimates include networking equipment, storage, liquid cooling systems, electrical infrastructure, and power.
A single GW can power roughly 700,000 to 1 million U.S. homes.
So are high costs a business moat or a sign of dangerous costs, or perhaps both?
Morgan Stanley also projected that the combined capital expenditure of the five largest AI-driven firms (Microsoft, Google, Amazon, Meta, and SpaceX) will reach approximately $1.2 trillion and $1.4 trillion in 2027 and 2028, respectively.
By 2028, available computing capacity is expected to grow from around 30 GW in 2025 to nearly 120 GW, a fourfold increase.
Morgan Stanley expects the combined available computing capacity of the five major hyperscalers to reach nearly 120 GW by 2028, a fourfold increase from approximately 30 GW in 2025.
AWS will have the largest capacity in 2028 at 35 GW, followed by Google at 31 GW. Meta’s capacity is projected to grow from approximately 3.5 GW at the end of 2025 to 14 GW in 2027 and 21 GW in 2028.
But the AI compute arms race is shifting from "how much you build" to "how much you sell,” Morgan Stanley suggests. Who can convert compute into monetizable revenue streams?
Copyright is a tricky business, and has been since the advent of digital media. But artificial intelligence likely will cause a rethinking or adaptation of copyright, in part because it is getting harder to distinguish between a human author’s particular formulation of an idea and an AI-generated alternative.
Traditionally, copyright has been designed to encourage innovation by providing creators with limited monopolies over their work, protecting the particular expressions of ideas, but not the ideas themselves.
So copyright protects an author’s expression of an idea, not ideas, facts, or styles themselves.
Abundance, instead of scarcity, partly explains why that is the case.
Copyright traditionally assumed scarcity and control over physical copies (books, records, films). All that became more challenging when digital distribution; the internet; user-generated content and easy remixing of content replaced scarcity and distribution cost:
Digital formats (MP3s, JPEGs, PDFs) allowed lossless copying at near-zero cost, unlike analog media. Napster (late 1990s) and peer-to-peer sharing exemplified mass infringement
Global, instantaneous sharing by websites, torrents, and streaming bypassed traditional gatekeepers
Social media, YouTube and many other sites blurred lines between consumers and creators, increasing derivative works, remixes, and mashups
Generative AI arguably complicated matters further:
Model training is based on use of massive datasets, often scraped from the internet, raising questions of reproduction rights. AI companies argue it's necessary for learning patterns/ideas (not protected expression); creators call it systemic infringement.
AI can generate content mimicking styles, potentially causing "market dilution" as a new issue
AI outputs generally lack human authorship for copyright, but human-prompted or edited works are gray areas
Training data is often non-transparent and opt-out mechanisms are impractical at scale
U.S. courts show diverging fair use rulings; reports from U.S. Copyright Office, UK, EU Parliament highlight needs for licensing, transparency, or new frameworks (e.g., compulsory licensing debates).
The main policy choices are whether to create new rules for training data, how to treat AI-generated outputs and whether to add special protections for likeness, voice, or style cloning.
The core tradeoffs, as always, are between innovation and creator compensation. Broader licensing can raise costs for AI developers and small firms, but it may also reduce litigation and give rights holders a clearer market.
Looser rules can speed model development, but they may weaken incentives for human creators.
For now, the U.S. Copyright Office says existing copyright principles are flexible enough to handle AI.
Observers say artificial intelligence often changes “how work is done” or “how well work is done” (quality improvements) rather than just “how fast it is done,” leading to outcomes that are difficult to capture in traditional productivity statistics.
That is particularly true for intangible products such as software, e-books, downloadable music, mobile applications, healthcare consultations, financial advice, legal services, streaming subscriptions, web hosting or any other product that is experiential (haircuts or live concerts).
Product quality changes, called “hedonic,” are particularly hard to quantify in these cases. Among the classic examples are personal computers that, over time, incorporate faster processors, more memory, better user interfaces, displays or audio, but without a price increase.
Smartphones might add premium materials, for example.
The point is that much of AI’s value is qualitative: improved decision-making, better user experiences, or reduced risk in complex processes (like drug discovery) that will not always show up as an immediate increase in volume-based output.
And all that is hard to measure.
Proxy Metric
What it Measures
Limitation
Task Completion Time
How much faster a specific, defined task is finished with AI.
Ignores quality variance and "rework" time (verification).
User/Adoption Rates
The percentage of the workforce actively using AI tools.
Does not measure value or net efficiency gains.
Resource Optimization
Reduction in compute or operational costs for a given output.
Can hide negative impacts on employee skill formation.
User Satisfaction
Improved quality of output or speed as perceived by the customer.
Subjective and may not correlate to bottom-line profitability.
Error/Defect Rates
Frequency of mistakes or need for human intervention in AI tasks.
Often hard to track consistently across different workflows.
That is not unusual for general-purpose technologies such as electricity or the internet. But financial analysts want quantitative metrics, so industries will develop them.
Industry
Metric Category
Specific Proxy Metric
Manufacturing
Operational Efficiency
Reduction in equipment downtime (via predictive maintenance).
Healthcare
Clinical Efficiency
Time reduction for diagnostic tasks or patient documentation.
Retail
Revenue & Customer
Increase in conversion rates or uplift in average order value.
Finance
Risk & Compliance
Reduction in fraud false-positive rates or manual audit hours.
Cross-Industry
Strategic Value
Revenue generated from AI-enabled new product lines.
Cross-Industry
Human Capital
Shift in employee time from routine tasks to high-margin work.
All of these metrics can be imprecise. It can be hard to isolate AI impact from all other organizational processes, for example.
Does use of artificial intelligence necessarily pose the risk of diminishing critical thinking or thinking skills? The answer might well depend on how AI is used.
But that is true of many human endeavors. People rarely stop engaging in activities they find intrinsically rewarding simply because technology makes the outcome easier to obtain.
As much as I enjoy this clip of waveriding at a favorite spot, I'd much rather be doing it.
Humans experience deep satisfaction when engaged in challenging activities that match their abilities. The reward is not merely the finished product but the experience of making it.
That is why people:
climb mountains despite vehicle access
bake bread despite supermarkets
garden despite grocery stores
play golf despite television coverage.
The activity itself provides enjoyment. If people only cared about outcomes, very few would actually play sports.
And AI may affect creative work in much the same way.
Calculators changed mathematics, for example. They reduced arithmetic effort but did not eliminate the need to formulate problems or interpret results.
There is a reasonable argument to be made that outsourcing “writing” to a language model poses a risk.
For many writers, that is a relatively negligible risk, since many writers compose because they enjoy the process of writing, and it makes no sense to outsource the “fun” of writing at all.
For many writers, writing is enjoyable because it combines:
discovery
exploration
self-expression
problem solving
craftsmanship.
The point is that technological advances rarely eliminate hobbies.
Photography did not eliminate painting
Recorded music did not eliminate amateur musicians
Power tools did not eliminate woodworking
GPS did not eliminate hiking
Word processors did not eliminate writing.
For many forms of knowledge work, AI is not replacing thinking so much as changing where the thinking occurs.
Instead of spending most of one's effort locating information, more of the cognitive effort shifts to:
Framing the problem before asking AI
Evaluating and challenging the response afterward
Synthesizing the results into an original conclusion.
So, in many research and knowledge-work settings, AI functions less as a replacement for thinking than as an accelerator for information retrieval and synthesis.
The important intellectual work frequently occurs before the AI interaction (defining the problem, framing the question) and after it (evaluating, integrating, and applying the results).
That is similar to how experienced researchers have long used search engines and databases, for example, and suggests that “how” AI is used matters.
And experts might benefit from using AI more than novices, as they are able to formulate better questions, based on:
relevant mental models
domain knowledge
intuition about good questions
ability to detect errors.
Stage
Traditional research
AI-assisted research
Where critical thinking occurs
Define question
Formulate hypothesis
Formulate prompt/problem
Very high
Gather information
Library, databases, Google
AI search or LLM
Moderate
Evaluate evidence
Read sources
Verify AI claims and sources
Very high
Compare viewpoints
Read competing authors
Ask AI to generate opposing views
Very high
Draw conclusions
Human synthesis
Human synthesis
Very high
Communicate findings
Human writing
Human writing (possibly AI-assisted editing)
High
Perhaps there is an analogy to the use of “search.” When Google became dominant around 2000, educators raised similar concerns about search making us dumber.
Instead, search shifted the balance of cognitive work away from memorization and toward higher-level reasoning.
AI might be similar, in many instances.
Thought traditional search asks users to perform much of the information synthesis themselves (find sources and evaluate them),
AI saves time by summarizing results.
But it does not eliminate the need to ask:
Is this correct?
Is something missing?
What assumptions underlie this answer?
What evidence contradicts it?
Are these the strongest sources?
Skeptics will note that many users will not take the time to do so. But that arguably was the case beforehand.
Rather than replacing reasoning, AI often expands the range of questions that can be explored within a fixed amount of time.
Also, there are some techniques that encourage broader and deeper exploration.
Recent research describes phenomena such as "cognitive offloading," "epistemic atrophy," and an "illusion of understanding," where users mistake fluent explanations for genuine comprehension. These effects appear strongest when AI substitutes for independent evaluation rather than supporting it. (Business Insider)
For experienced researchers, AI is often best understood as an unusually capable research assistant rather than an autonomous thinker.
But the researcher still bears responsibility for asking the right questions, verifying sources, weighing evidence, and integrating insights into an original conclusion.
In that sense, AI resembles an evolution of search rather than a replacement for thought.
The cognitive work shifts away from locating information and toward framing, evaluating, and synthesizing it.
AI substantially reduces the effort required for search, retrieval, summarization, and drafting, but arguably does not eliminate the need for problem formulation, judgment, skepticism, synthesis, or decision-making.
Whether critical thinking declines depends less on the technology than on whether users treat AI as an answer machine or as a research collaborator whose outputs require evaluation.
For writers who enjoy the process of writing, AI is not a replacement, anymore than watching surfing is a replacement for surfing.
